Regularized reduced rank regression for mixed predictor and response variables
Bibliographic Data
| ID | 22410142 |
|---|---|
| Authors | Lorenza Cotugno (0009-0008-6752-1392, University of Naples Federico II Naples Italy, corresponding author), Mark de Rooij (0000-0001-7308-6210, Leiden University Leiden The Netherlands), Roberta Siciliano (0000-0002-8012-0461, University of Naples Federico II Naples Italy) |
| Year | 2026 |
| Publication date | 2026-06-10 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Mathematical and Statistical Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0007-1102 • E-ISSN: 2044-8317 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bmsp.70057 |
| PMID | 42272067 |
| OpenAlex | W4416592732 |
| Language | EN |
| References cited | 38 |
In this paper, we introduce the Generalized Mixed Regularized Reduced Rank Regression model (GMR 4 ), an extension of the GMR 3 model designed to improve performance in high‐dimensional settings. GMR 3 is a regression method for a mix of numeric, binary and ordinal response variables, while also allowing for mixed‐type predictors through optimal scaling. GMR 4 extends this approach by incorporating regularization techniques, such as Ridge, Lasso, Group Lasso, or any combination thereof, making the model suitable for datasets with a large number of predictors or collinearity among them. In addition, we propose a cross‐validation procedure that enables the estimation of the rank and the penalty parameter . Through a simulation study, we evaluate the performance of the model under different scenarios, varying the sample size, the number of non‐informative predictors and response dimension. The results of the simulation study guide the choice of the penalty parameter in the empirical application ISSP: Health and Healthcare I–II (2023), which includes mixed‐type predictors and ordinal responses. In this application, the model results in a sparse and interpretable solution, with a limited set of influential predictors that provide insights into public attitudes towards health care.
Collinearity · Covariate · Ordinal data · Ordinal regression · Rank (graph theory) · Regression · Regression analysis · Regularization (linguistics) · Segmented regression · Advanced Statistical Methods and Models · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
Introducing Multilevel Modeling
On the Theory of Scales of Measurement
Regularization and Variable Selection Via the Elastic Net
Model Selection and Estimation in Regression with Grouped Variables
Ridge Regression
Regression Models for Ordinal Data
Inference and missing data
Regression Shrinkage and Selection Via the Lasso
| Highly cited | No |
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